In the recent past, TMP has received considerable interest among the research community [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. The primary challenge of planning in the hybrid space is to obtain an efficient mapping between the discrete task and continuous motion levels. The de facto standard syntax for task planning is the Planning Domain Definition Language (PDDL) [7]} and most approaches resort to the same for task planning. Semantic attachments are used in [8]}, [9]}, [3]}, [5]}, associating algorithms to functions and predicate symbols via external procedures. Though semantic attachments allow mapping between the task and motion space, the environment is assumed to be known before-hand. Besides, the robot configuration, grasp poses need to be specified in advance and renders the continuous motion space to be finite. This a priori discretization is relaxed in [12]}, [2]}, [14]}, [4]}. Yet, these approaches are domain specific in nature. Moreover, most approaches (for example [16]}, [2]}, [3]}) first compute a task-level plan and refines it until a feasible motion plan is found or until timeout. The approach in [5]}, however, checks for the motion feasibly as each task-level action is expanded by the task planner. But this approach assumes a pre-discretized motion space and thereby a finite action set. Recent work of Caelan et al. [20]} address this limitation by introducing streams within PDDL, which enable procedures for sampling values of continuous variables and thereby encoding an infinite set of actions.
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